Research in Higher Education

, Volume 57, Issue 6, pp 682–713 | Cite as

How Many Credits Should an Undergraduate Take?

  • Paul Attewell
  • David MonaghanEmail author


Low completion rates and increased time to degree at U.S. colleges are a widespread concern for policymakers and academic leaders. Many ‘full time’ undergraduates currently enroll at 12 credits per semester despite the fact that a bachelor’s degree cannot be completed within 4 years at that credit-load. The academic momentum perspective holds that if, at the beginning of their first year in college, undergraduates attempted more course credits per semester, then overall graduation rates could rise. Using nationally-representative data and propensity-score matching methods to reduce selection bias, we find that academically and socially similar students who initially attempt 15 rather than 12 credits do graduate at significantly higher rates within 6 years of initial enrollment. We also find that students who increase their credit load from below fifteen to fifteen or more credits in their second semester are more likely to complete a degree within 6 years than similar students who stay below this threshold. Our evidence suggests that stressing a norm that full time enrollment should be 15 credits per semester would improve graduation rates for most kinds of students. However, an important caveat is that those undergraduates whose paid work exceeds 30 h per week do not appear to benefit from taking a higher course load.


Academic momentum Credit load College completion Propensity score matching 



This research was funded through a grant from the Bill and Melinda Gates foundation.


  1. Adelman, C. (1999). Answers in the tool box. Washington, DC: US Department of Education.Google Scholar
  2. Adelman, C. (2004). Principal indicators of student academic histories in postsecondary education, 1972–2000. Washington, DC: US Department of Education.Google Scholar
  3. Adelman, C. (2006). The toolbox revisited: Paths to degree completion from high school through college. Washington, DC: US Department of Education.Google Scholar
  4. Astin, A. (1984). Student involvement: A developmental theory for higher education. Journal of College Student Personnel, 25(3), 297–308.Google Scholar
  5. Attewell, P., & Domina, T. (2008). Raising the bar: Curricular intensity and academic performance. Educational Evaluation and Policy Analysis, 30(1), 51–71.CrossRefGoogle Scholar
  6. Attewell, P., Heil, S., & Reisel, L. (2011). Competing explanations of undergraduate noncompletion. American Educational Research Journal, 48(3), 536–559.CrossRefGoogle Scholar
  7. Attewell, P., Heil, S., & Reisel, L. (2012). What is academic momentum? And does it matter? Educational Evaluation and Policy Analysis, 34(1), 27–44.CrossRefGoogle Scholar
  8. Attewell, P., & Jang, S. H. (2013). Summer coursework and completing college. Research in Higher Education Journal, 20(1), 117–141.Google Scholar
  9. Aud, S., Hussar, W., Johnson, F., Kena, G., Roth, E., Manning, E., et al. (2012). The condition of education 2012 (NCES 2012-045). Washington, DC: National Center for Education Statistics, U.S. Department of Education.Google Scholar
  10. Austin, P. C. (2011). An introduction to propensity score methods for reducing the effects of confounding in observational studies. Multivariate Behavioral Research, 46(3), 399–424.CrossRefGoogle Scholar
  11. Bailey, M. J., & Dynarski, S. M. (2011). Inequality in postsecondary education. In G. J. Duncan & R. J. Murname (Eds.), Whither opportunity? (pp. 117–132). New York: Russel Sage.Google Scholar
  12. Baum, S., Dynarski, S., Hauptman, A., Long, B. T., McPherson, M., Scott-Clayton, J., & Turner, S. (2011). Letter to the president of the college board. Retrieved from
  13. Bean, J. P., & Metzner, B. S. (1985). A conceptual model of nontraditional undergraduate student attrition. Review of Educational Research, 55(4), 485–540.CrossRefGoogle Scholar
  14. Becker, S. O., & Caliendo, M. (2007). Mhbounds—sensitivity analysis for average treatment effects. IZA Discussion Papers, No. 2542. Retrieved from
  15. Bettinger, E. P., & Long, B. T. (2009). Addressing the needs of underprepared students in higher education: Does college remediation work? Journal of Human Resources, 44(3), 736–771.CrossRefGoogle Scholar
  16. Bloom, D., & Sommo, C. (2005). Building learning communities: Early results from the opening doors demonstration at Kingsborough Community College. New York: MDRC.Google Scholar
  17. Bong, M., & Skaalvik, E. M. (2003). Academic self-concept and self-efficacy: How different are they really? Educational Psychology Review, 15(1), 1–40.CrossRefGoogle Scholar
  18. Bound, J., Lovenheim, M. F., & Turner, S. (2010). Why have college completion rates declined? An analysis of changing student preparation and collegiate resources. American Journal of Applied Economics, 2(3), 129–157.CrossRefGoogle Scholar
  19. Bound, J., Lovenheim, M. F., & Turner, S. (2012). Increasing time to baccalaureate degree in the United States. Education, 7(4), 375–424.Google Scholar
  20. Bourdieu, P. (1986). The forms of capital. In J. Richardson (Ed.), Handbook of theory and research for the sociology of education (pp. 241–258). New York: Greenwood.Google Scholar
  21. Bourdieu, P., & Passeron, C. (1977). Reproduction: In education, culture, and society. London: Sage.Google Scholar
  22. Bowen, W. G., & Bok, D. (1998). The shape of the river: Long-term consequences of considering race in college and university admissions. Princeton, NJ: Princeton University Press.Google Scholar
  23. Bowen, W. G., Chingos, M. M., & McPherson, M. S. (2009). Crossing the finish line: Completing college at America’s public universities. Princeton: Princeton University Press.Google Scholar
  24. Brand, J. E., & Halaby, C. N. (2006). Regression and matching estimates of the effects of elite college attendance on educational and career achievement. Social Science Research, 35(3), 749–770.CrossRefGoogle Scholar
  25. Caliendo, M., & Kopeinig, S. (2008). Some practical guidance for the implementation of propensity score matching. Journal of Economic Surveys, 22(1), 31–72.CrossRefGoogle Scholar
  26. Cataldi, E. F., Green, C., Henke, R., Lew, T., Woo, J., Shepherd, B., et al. (2011). 2008–09 baccalaureate and beyond longitudinal study (B&B:08/09) first look (NCES Report 2011-236). Washington DC: US Department of Education.Google Scholar
  27. Cohodes, S., & Goodman, J. (2012). First degree earns: The impact of college quality on college completion rates (HKS Faculty Research Working Paper Series RWP12-033), Cambridge, MA: John F. Kennedy School of Government, Harvard University.Google Scholar
  28. College Board. (2008). Coming to our senses: Education and the American future. New York: College Board.Google Scholar
  29. Complete College America. (2013). The power of 15 credits: Enrollment intensity and postsecondary student achievement. Washington, D.C.: Complete College America. Retrieved from
  30. Conley, D. (2001). Capital for college: Parental assets and postsecondary schooling. Sociology of Education, 74, 59–72.CrossRefGoogle Scholar
  31. Dowd, A. C., & Coury, T. (2006). The effect of loans on the persistence and attainment of community college students. Research in Higher Education, 47(1), 33–62.CrossRefGoogle Scholar
  32. Doyle, W. R. (2011). Effect of increased academic momentum on transfer rates: An application of the generalized propensity score. Economics of Education Review, 30(1), 191–200.CrossRefGoogle Scholar
  33. Dumais, S. A., & Ward, A. (2010). Cultural capital and first-generation college success. Poetics, 38(3), 245–265.CrossRefGoogle Scholar
  34. Dynarski, S. (2003). Does aid matter? Measuring the effect of student aid on college attendance and completion. American Economic Review, 93(1), 279–288.CrossRefGoogle Scholar
  35. Frölich, M. (2005). Matching estimators and optimal bandwidth choice. Statistics and Computing, 15(3), 197–215.CrossRefGoogle Scholar
  36. Gamoran, A. (1987). The stratification of high school learning opportunities. Sociology of Education, 60(3), 135–155.CrossRefGoogle Scholar
  37. Gamoran, A., & Hannigan, E. C. (2000). Algebra for everyone? Benefits of college-preparatory mathematics for students with diverse abilities in early secondary school. Educational Evaluation and Policy Analysis, 22(3), 241–254.CrossRefGoogle Scholar
  38. Gamoran, A., & Mare, R. D. (1989). Secondary school tracking and educational inequality: Compensation, reinforcement, or neutrality? The American Journal of Sociology, 94(5), 1146–1183.CrossRefGoogle Scholar
  39. Goldrick-Rab, S. (2007). Promoting academic momentum at community colleges: Challenges and opportunities (CCRC Working Paper No.5). New York: Community College Research Center.Google Scholar
  40. Guo, S., & Fraser, M. W. (2010). Propensity score analysis: Statistical methods and applications. Los Angeles: Sage.Google Scholar
  41. Heckman, J., Ichimura, H., Smith, J., & Todd, P. (1998a). Characterizing selection bias using experimental data. Econometrica, 66, 1017–1098.CrossRefGoogle Scholar
  42. Heckman, J. J., Ichimura, H., & Todd, P. E. (1997). Matching as an econometric evaluation estimator: Evidence from evaluating a job training programme. The Review of Economic Studies, 64(4), 605–654.CrossRefGoogle Scholar
  43. Heckman, J. J., Ichimura, H., & Todd, P. (1998b). Matching as an econometric evaluation estimator. The Review of Economic Studies, 65(2), 261–294.CrossRefGoogle Scholar
  44. Hussar, W. J., & Bailey, T. M. (2013). Projections of education statistics to 2022 (NCES 2014-051). Washington, DC: National Center for Education Statistics, U.S. Department of Education.Google Scholar
  45. Ichino, A., Mealli, F., & Nannicini, T. (2008). From temporary help jobs to permanent employment: What can we learn from matching estimators and their sensitivity? Journal of Applied Econometrics, 23(3), 305–327.CrossRefGoogle Scholar
  46. Jackson, J., & Kurlaender, M. (2014). College readiness and college completion at broad-access four-year institutions. American Behavioral Scientist, 58(8), 947–971.CrossRefGoogle Scholar
  47. Kelly, A. P., & Schneider, M. (2012). Getting to graduation: The completion agenda in higher education. Baltimore: The Johns Hopkins University Press.Google Scholar
  48. Lareau, A., & Weininger, E. B. (2003). Cultural capital in educational research: a critical assessment. Theory and Society, 32(5), 567–606.CrossRefGoogle Scholar
  49. Lechner, M. (2002). Program heterogeneity and propensity score matching: An application to the evaluation of active labor market policies. Review of Economics and Statistics, 84(2), 205–220.CrossRefGoogle Scholar
  50. Leuven, E., & Sianesi, B. (2003). PSMATCH2: Stata module to perform full Mahalanobis and propensity score matching, common support graphing, and covariate imbalance testing, version 1.2.3. Retrieved from
  51. Long, M. C., Conger, D., & Iatarola, P. (2012). Effects of high school course-taking on secondary and postsecondary success. American Educational Research Journal, 49(2), 285–322.CrossRefGoogle Scholar
  52. Lucas, S. R. (2001). Effectively maintained inequality: Education transitions, track mobility, and social background effects. American Journal of Sociology, 106(6), 1642–1690.CrossRefGoogle Scholar
  53. Martin, A. J., Wilson, R., Liem, G. A. D., & Ginns, P. (2013). Academic momentum at university/college: Exploring the roles of prior learning, life experience, and ongoing performance in academic achievement across time. The Journal of Higher Education, 84(5), 640–674.CrossRefGoogle Scholar
  54. McCormick, A., & Horn, L. J. (1996). A descriptive summary of 1992–93 bachelor’s degree recipients: 1 year later, with an essay on time to degree (NCES Report 96-158). Washington DC: US Department of Education.Google Scholar
  55. Morgan, S., & Winship, C. (2007). Counterfactuals and causal inference. New York: Cambridge University Press.CrossRefGoogle Scholar
  56. Nannicini, T. (2007). Simulation-based sensitivity analysis for matching estimators. Stata Journal, 7(3), 334.Google Scholar
  57. National Center for Education Statistics. (2011). 2004/2009 Beginning postsecondary students longitudinal study restricted-use data file (NCES No. 2011244). Washington, DC: Institute for Education Sciences, Department of Education.Google Scholar
  58. National Center for Education Statistics. (2012). 2004/2009 Beginning postsecondary students longitudinal study restricted-use transcript data files and documentation (NCES 2012243). Washington, D.C: National Center for Education Statistics, Department of Education.Google Scholar
  59. Riegle-Crumb, C. (2006). The path through math: Course sequences and academic performance at the intersection of race/ethnicity and gender. American Journal of Education, 113(1), 1–17.CrossRefGoogle Scholar
  60. Rosenbaum, P. R. (1987). Sensitivity analysis for certain permutation inferences in matched observational studies. Biometrika, 74(1), 13–26.CrossRefGoogle Scholar
  61. Rosenbaum, P. R. (2002). Observational studies. New York: Springer.CrossRefGoogle Scholar
  62. Rosenbaum, P. R., & Rubin, D. B. (1983a). The central role of the propensity score in observational studies for causal effects. Biometrika, 70, 41–55.CrossRefGoogle Scholar
  63. Rosenbaum, P. R., & Rubin, D. B. (1983b). Assessing the sensitivity to an unobserved binary covariate in an observational study with binary outcome. Journal of the Royal Statistical Society: Series B (Methodological), 45, 212–218.Google Scholar
  64. Rosenbaum, P. R., & Rubin, D. B. (1985). Constructing a control group using multivariate matched sampling methods that incorporate the propensity score. The American Statistician, 39(1), 33–38.Google Scholar
  65. Rubin, D. B. (1973). Matching to remove bias in observational studies. Biometrics, 29, 159–183.CrossRefGoogle Scholar
  66. Scott-Clayton, J. (2011). On money and motivation: A quasi-experimental analysis of financial incentives for college achievement. Journal of Human Resources, 46(3), 614–646.CrossRefGoogle Scholar
  67. Stuart, E. A. (2010). Matching methods for causal inference: A review and a look forward. Statistical Science: A Review Journal of the Institute of Mathematical Statistics, 25(1), 1–21.CrossRefGoogle Scholar
  68. Szafran, R. F. (2001). The effect of academic load on success for new college students: Is lighter better? Research in Higher Education, 42(1), 27–50.CrossRefGoogle Scholar
  69. Tanuguchi, H., & Kaufman, G. (2005). Degree completion among nontraditional college students. Social Science Quarterly, 86(4), 912–927.CrossRefGoogle Scholar
  70. Tinto, V. (1975). Dropout from higher education: A theoretical synthesis of recent research. Review of Educational Research, 45, 89–125.CrossRefGoogle Scholar
  71. Turner, S. (2004). Going to college and finishing college. Explaining different educational outcomes. In C. M. Hoxby (Ed.), College choices: The economics of where to go, when to go, and how to pay for it (pp. 13–62). Chicago: University of Chicago Press.CrossRefGoogle Scholar
  72. US Department of Education. (2006). A test of leadership: charting the future of U.S. higher education. Washington DC: US Department of Education.Google Scholar
  73. Zimmerman, B. J., Bandura, A., & Martinez-Pons, M. (1992). Self-motivation for academic attainment: The role of self-efficacy beliefs and personal goal setting. American Educational Research Journal, 29(3), 663–676.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  1. 1.The Graduate CenterThe City University of New York (CUNY)New YorkUSA
  2. 2.Wisconsin HOPE LabUniversity of Wisconsin-MadisonMadisonUSA

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